# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Adds electrical generators and existing hydro storage units to a base network.
Relevant Settings
-----------------
.. code:: yaml
costs:
year:
version:
dicountrate:
emission_prices:
electricity:
max_hours:
marginal_cost:
capital_cost:
conventional_carriers:
co2limit:
extendable_carriers:
estimate_renewable_capacities:
load:
scaling_factor:
renewable:
hydro:
carriers:
hydro_max_hours:
hydro_capital_cost:
lines:
length_factor:
.. seealso::
Documentation of the configuration file ``config.yaml`` at :ref:`costs_cf`,
:ref:`electricity_cf`, :ref:`load_cf`, :ref:`renewable_cf`, :ref:`lines_cf`
Inputs
------
- ``resources/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``data/bundle/hydro_capacities.csv``: Hydropower plant store/discharge power capacities, energy storage capacity, and average hourly inflow by country.
.. image:: ../img/hydrocapacities.png
:scale: 34 %
- ``data/geth2015_hydro_capacities.csv``: alternative to capacities above; not currently used!
- ``resources/load.csv`` Hourly per-country load profiles.
- ``resources/regions_onshore.geojson``: confer :ref:`busregions`
- ``resources/nuts3_shapes.geojson``: confer :ref:`shapes`
- ``resources/powerplants.csv``: confer :ref:`powerplants`
- ``resources/profile_{}.nc``: all technologies in ``config["renewables"].keys()``, confer :ref:`renewableprofiles`.
- ``networks/base.nc``: confer :ref:`base`
Outputs
-------
- ``networks/elec.nc``:
.. image:: ../img/elec.png
:scale: 33 %
Description
-----------
The rule :mod:`add_electricity` ties all the different data inputs from the preceding rules together into a detailed PyPSA network that is stored in ``networks/elec.nc``. It includes:
- today's transmission topology and transfer capacities (optionally including lines which are under construction according to the config settings ``lines: under_construction`` and ``links: under_construction``),
- today's thermal and hydro power generation capacities (for the technologies listed in the config setting ``electricity: conventional_carriers``), and
- today's load time-series (upsampled in a top-down approach according to population and gross domestic product)
It further adds extendable ``generators`` with **zero** capacity for
- photovoltaic, onshore and AC- as well as DC-connected offshore wind installations with today's locational, hourly wind and solar capacity factors (but **no** current capacities),
- additional open- and combined-cycle gas turbines (if ``OCGT`` and/or ``CCGT`` is listed in the config setting ``electricity: extendable_carriers``)
"""
import logging
from _helpers import configure_logging, update_p_nom_max
import pypsa
import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import powerplantmatching as pm
from powerplantmatching.export import map_country_bus
from vresutils import transfer as vtransfer
idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def normed(s): return s/s.sum()
def calculate_annuity(n, r):
"""Calculate the annuity factor for an asset with lifetime n years and
discount rate of r, e.g. annuity(20, 0.05) * 20 = 1.6"""
if isinstance(r, pd.Series):
return pd.Series(1/n, index=r.index).where(r == 0, r/(1. - 1./(1.+r)**n))
elif r > 0:
return r / (1. - 1./(1.+r)**n)
else:
return 1 / n
def _add_missing_carriers_from_costs(n, costs, carriers):
missing_carriers = pd.Index(carriers).difference(n.carriers.index)
if missing_carriers.empty: return
emissions_cols = costs.columns.to_series()\
.loc[lambda s: s.str.endswith('_emissions')].values
suptechs = missing_carriers.str.split('-').str[0]
emissions = costs.loc[suptechs, emissions_cols].fillna(0.)
emissions.index = missing_carriers
n.import_components_from_dataframe(emissions, 'Carrier')
def load_costs(tech_costs, config, elec_config, Nyears=1.):
# set all asset costs and other parameters
costs = pd.read_csv(tech_costs, index_col=[0,1]).sort_index()
# correct units to MW
costs.loc[costs.unit.str.contains("/kW"),"value"] *= 1e3
costs.unit = costs.unit.str.replace("/kW", "/MW")
fill_values = config["fill_values"]
costs = costs.value.unstack().fillna(fill_values)
costs["capital_cost"] = ((calculate_annuity(costs["lifetime"], costs["discount rate"]) +
costs["FOM"]/100.) *
costs["investment"] * Nyears)
costs.at['OCGT', 'fuel'] = costs.at['gas', 'fuel']
costs.at['CCGT', 'fuel'] = costs.at['gas', 'fuel']
costs['marginal_cost'] = costs['VOM'] + costs['fuel'] / costs['efficiency']
costs = costs.rename(columns={"CO2 intensity": "co2_emissions"})
costs.at['OCGT', 'co2_emissions'] = costs.at['gas', 'co2_emissions']
costs.at['CCGT', 'co2_emissions'] = costs.at['gas', 'co2_emissions']
costs.at['solar', 'capital_cost'] = config["rooftop_share"] * costs.at['solar-rooftop', 'capital_cost'] + \
(1-config["rooftop_share"]) * costs.at['solar-utility', 'capital_cost']
def costs_for_storage(store, link1, link2=None, max_hours=1.):
capital_cost = link1['capital_cost'] + max_hours * store['capital_cost']
if link2 is not None:
capital_cost += link2['capital_cost']
return pd.Series(dict(capital_cost=capital_cost,
marginal_cost=0.,
co2_emissions=0.))
max_hours = elec_config['max_hours']
costs.loc["battery"] = \
costs_for_storage(costs.loc["battery storage"], costs.loc["battery inverter"],
max_hours=max_hours['battery'])
costs.loc["H2"] = \
costs_for_storage(costs.loc["hydrogen storage underground"], costs.loc["fuel cell"],
costs.loc["electrolysis"], max_hours=max_hours['H2'])
for attr in ('marginal_cost', 'capital_cost'):
overwrites = config.get(attr)
if overwrites is not None:
overwrites = pd.Series(overwrites)
costs.loc[overwrites.index, attr] = overwrites
return costs
def load_powerplants(ppl_fn):
carrier_dict = {'ocgt': 'OCGT', 'ccgt': 'CCGT', 'bioenergy': 'biomass',
'ccgt, thermal': 'CCGT', 'hard coal': 'coal'}
return (pd.read_csv(ppl_fn, index_col=0, dtype={'bus': 'str'})
.powerplant.to_pypsa_names()
.rename(columns=str.lower)
.replace({'carrier': carrier_dict}))
def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.):
substation_lv_i = n.buses.index[n.buses['substation_lv']]
regions = (gpd.read_file(regions).set_index('name')
.reindex(substation_lv_i))
opsd_load = (pd.read_csv(load, index_col=0, parse_dates=True)
.filter(items=countries))
logger.info(f"Load data scaled with scalling factor {scaling}.")
opsd_load *= scaling
nuts3 = gpd.read_file(nuts3_shapes).set_index('index')
def upsample(cntry, group):
l = opsd_load[cntry]
if len(group) == 1:
return pd.DataFrame({group.index[0]: l})
else:
nuts3_cntry = nuts3.loc[nuts3.country == cntry]
transfer = vtransfer.Shapes2Shapes(group, nuts3_cntry.geometry,
normed=False).T.tocsr()
gdp_n = pd.Series(transfer.dot(nuts3_cntry['gdp'].fillna(1.).values),
index=group.index)
pop_n = pd.Series(transfer.dot(nuts3_cntry['pop'].fillna(1.).values),
index=group.index)
# relative factors 0.6 and 0.4 have been determined from a linear
# regression on the country to continent load data
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
return pd.DataFrame(factors.values * l.values[:,np.newaxis],
index=l.index, columns=factors.index)
load = pd.concat([upsample(cntry, group) for cntry, group
in regions.geometry.groupby(regions.country)], axis=1)
n.madd("Load", substation_lv_i, bus=substation_lv_i, p_set=load)
def update_transmission_costs(n, costs, length_factor=1.0):
# TODO: line length factor of lines is applied to lines and links.
# Separate the function to distinguish.
n.lines['capital_cost'] = (n.lines['length'] * length_factor *
costs.at['HVAC overhead', 'capital_cost'])
if n.links.empty: return
dc_b = n.links.carrier == 'DC'
# If there are no dc links, then the 'underwater_fraction' column
# may be missing. Therefore we have to return here.
if n.links.loc[dc_b].empty: return
costs = (n.links.loc[dc_b, 'length'] * length_factor *
((1. - n.links.loc[dc_b, 'underwater_fraction']) *
costs.at['HVDC overhead', 'capital_cost'] +
n.links.loc[dc_b, 'underwater_fraction'] *
costs.at['HVDC submarine', 'capital_cost']) +
costs.at['HVDC inverter pair', 'capital_cost'])
n.links.loc[dc_b, 'capital_cost'] = costs
def attach_wind_and_solar(n, costs, input_profiles, technologies, extendable_carriers, line_length_factor=1):
# TODO: rename tech -> carrier, technologies -> carriers
_add_missing_carriers_from_costs(n, costs, technologies)
for tech in technologies:
if tech == 'hydro':
continue
with xr.open_dataset(getattr(input_profiles, 'profile_' + tech)) as ds:
if ds.indexes['bus'].empty: continue
suptech = tech.split('-', 2)[0]
if suptech == 'offwind':
underwater_fraction = ds['underwater_fraction'].to_pandas()
connection_cost = (line_length_factor *
ds['average_distance'].to_pandas() *
(underwater_fraction *
costs.at[tech + '-connection-submarine', 'capital_cost'] +
(1. - underwater_fraction) *
costs.at[tech + '-connection-underground', 'capital_cost']))
capital_cost = (costs.at['offwind', 'capital_cost'] +
costs.at[tech + '-station', 'capital_cost'] +
connection_cost)
logger.info("Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}"
.format(connection_cost.min(), connection_cost.max(), tech))
else:
capital_cost = costs.at[tech, 'capital_cost']
n.madd("Generator", ds.indexes['bus'], ' ' + tech,
bus=ds.indexes['bus'],
carrier=tech,
p_nom_extendable=tech in extendable_carriers['Generator'],
p_nom_max=ds['p_nom_max'].to_pandas(),
weight=ds['weight'].to_pandas(),
marginal_cost=costs.at[suptech, 'marginal_cost'],
capital_cost=capital_cost,
efficiency=costs.at[suptech, 'efficiency'],
p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())
def attach_conventional_generators(n, costs, ppl, conventional_carriers, extendable_carriers, conventional_config, conventional_inputs):
carriers = set(conventional_carriers) | set(extendable_carriers['Generator'])
_add_missing_carriers_from_costs(n, costs, carriers)
ppl = (ppl.query('carrier in @carriers').join(costs, on='carrier', rsuffix='_r')
.rename(index=lambda s: 'C' + str(s)))
ppl["efficiency"] = ppl.efficiency.fillna(ppl.efficiency_r)
logger.info('Adding {} generators with capacities [GW] \n{}'
.format(len(ppl), ppl.groupby('carrier').p_nom.sum().div(1e3).round(2)))
n.madd("Generator", ppl.index,
carrier=ppl.carrier,
bus=ppl.bus,
p_nom_min=ppl.p_nom.where(ppl.carrier.isin(conventional_carriers), 0),
p_nom=ppl.p_nom.where(ppl.carrier.isin(conventional_carriers), 0),
p_nom_extendable=ppl.carrier.isin(extendable_carriers['Generator']),
efficiency=ppl.efficiency,
marginal_cost=ppl.marginal_cost,
capital_cost=ppl.capital_cost,
build_year=ppl.datein.fillna(0).astype(int),
lifetime=(ppl.dateout - ppl.datein).fillna(np.inf),
)
for carrier in conventional_config:
# Generators with technology affected
idx = n.generators.query("carrier == @carrier").index
for attr in list(set(conventional_config[carrier]) & set(n.generators)):
values = conventional_config[carrier][attr]
if f"conventional_{carrier}_{attr}" in conventional_inputs:
# Values affecting generators of technology k country-specific
# First map generator buses to countries; then map countries to p_max_pu
values = pd.read_csv(values, index_col=0).iloc[:, 0]
bus_values = n.buses.country.map(values)
n.generators[attr].update(n.generators.loc[idx].bus.map(bus_values).dropna())
else:
# Single value affecting all generators of technology k indiscriminantely of country
n.generators.loc[idx, attr] = values
def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **config):
_add_missing_carriers_from_costs(n, costs, carriers)
ppl = ppl.query('carrier == "hydro"').reset_index(drop=True)\
.rename(index=lambda s: str(s) + ' hydro')
ror = ppl.query('technology == "Run-Of-River"')
phs = ppl.query('technology == "Pumped Storage"')
hydro = ppl.query('technology == "Reservoir"')
country = ppl['bus'].map(n.buses.country).rename("country")
inflow_idx = ror.index.union(hydro.index)
if not inflow_idx.empty:
dist_key = ppl.loc[inflow_idx, 'p_nom'].groupby(country).transform(normed)
with xr.open_dataarray(profile_hydro) as inflow:
inflow_countries = pd.Index(country[inflow_idx])
missing_c = (inflow_countries.unique()
.difference(inflow.indexes['countries']))
assert missing_c.empty, (f"'{profile_hydro}' is missing "
f"inflow time-series for at least one country: {', '.join(missing_c)}")
inflow_t = (inflow.sel(countries=inflow_countries)
.rename({'countries': 'name'})
.assign_coords(name=inflow_idx)
.transpose('time', 'name')
.to_pandas()
.multiply(dist_key, axis=1))
if 'ror' in carriers and not ror.empty:
n.madd("Generator", ror.index,
carrier='ror',
bus=ror['bus'],
p_nom=ror['p_nom'],
efficiency=costs.at['ror', 'efficiency'],
capital_cost=costs.at['ror', 'capital_cost'],
weight=ror['p_nom'],
p_max_pu=(inflow_t[ror.index]
.divide(ror['p_nom'], axis=1)
.where(lambda df: df<=1., other=1.)))
if 'PHS' in carriers and not phs.empty:
# fill missing max hours to config value and
# assume no natural inflow due to lack of data
max_hours = config.get('PHS_max_hours', 6)
phs = phs.replace({'max_hours': {0: max_hours}})
n.madd('StorageUnit', phs.index,
carrier='PHS',
bus=phs['bus'],
p_nom=phs['p_nom'],
capital_cost=costs.at['PHS', 'capital_cost'],
max_hours=phs['max_hours'],
efficiency_store=np.sqrt(costs.at['PHS','efficiency']),
efficiency_dispatch=np.sqrt(costs.at['PHS','efficiency']),
cyclic_state_of_charge=True)
if 'hydro' in carriers and not hydro.empty:
hydro_max_hours = config.get('hydro_max_hours')
assert hydro_max_hours is not None, "No path for hydro capacities given."
hydro_stats = pd.read_csv(hydro_capacities,
comment="#", na_values='-', index_col=0)
e_target = hydro_stats["E_store[TWh]"].clip(lower=0.2) * 1e6
e_installed = hydro.eval('p_nom * max_hours').groupby(hydro.country).sum()
e_missing = e_target - e_installed
missing_mh_i = hydro.query('max_hours == 0').index
if hydro_max_hours == 'energy_capacity_totals_by_country':
# watch out some p_nom values like IE's are totally underrepresented
max_hours_country = e_missing / \
hydro.loc[missing_mh_i].groupby('country').p_nom.sum()
elif hydro_max_hours == 'estimate_by_large_installations':
max_hours_country = hydro_stats['E_store[TWh]'] * 1e3 / \
hydro_stats['p_nom_discharge[GW]']
missing_countries = (pd.Index(hydro['country'].unique())
.difference(max_hours_country.dropna().index))
if not missing_countries.empty:
logger.warning("Assuming max_hours=6 for hydro reservoirs in the countries: {}"
.format(", ".join(missing_countries)))
hydro_max_hours = hydro.max_hours.where(hydro.max_hours > 0,
hydro.country.map(max_hours_country)).fillna(6)
n.madd('StorageUnit', hydro.index, carrier='hydro',
bus=hydro['bus'],
p_nom=hydro['p_nom'],
max_hours=hydro_max_hours,
capital_cost=costs.at['hydro', 'capital_cost'],
marginal_cost=costs.at['hydro', 'marginal_cost'],
p_max_pu=1., # dispatch
p_min_pu=0., # store
efficiency_dispatch=costs.at['hydro', 'efficiency'],
efficiency_store=0.,
cyclic_state_of_charge=True,
inflow=inflow_t.loc[:, hydro.index])
def attach_extendable_generators(n, costs, ppl, carriers):
logger.warning("The function `attach_extendable_generators` is deprecated in v0.5.0.")
_add_missing_carriers_from_costs(n, costs, carriers)
for tech in carriers:
if tech.startswith('OCGT'):
ocgt = ppl.query("carrier in ['OCGT', 'CCGT']").groupby('bus', as_index=False).first()
n.madd('Generator', ocgt.index,
suffix=' OCGT',
bus=ocgt['bus'],
carrier=tech,
p_nom_extendable=True,
p_nom=0.,
capital_cost=costs.at['OCGT', 'capital_cost'],
marginal_cost=costs.at['OCGT', 'marginal_cost'],
efficiency=costs.at['OCGT', 'efficiency'])
elif tech.startswith('CCGT'):
ccgt = ppl.query("carrier in ['OCGT', 'CCGT']").groupby('bus', as_index=False).first()
n.madd('Generator', ccgt.index,
suffix=' CCGT',
bus=ccgt['bus'],
carrier=tech,
p_nom_extendable=True,
p_nom=0.,
capital_cost=costs.at['CCGT', 'capital_cost'],
marginal_cost=costs.at['CCGT', 'marginal_cost'],
efficiency=costs.at['CCGT', 'efficiency'])
elif tech.startswith('nuclear'):
nuclear = ppl.query("carrier == 'nuclear'").groupby('bus', as_index=False).first()
n.madd('Generator', nuclear.index,
suffix=' nuclear',
bus=nuclear['bus'],
carrier=tech,
p_nom_extendable=True,
p_nom=0.,
capital_cost=costs.at['nuclear', 'capital_cost'],
marginal_cost=costs.at['nuclear', 'marginal_cost'],
efficiency=costs.at['nuclear', 'efficiency'])
else:
raise NotImplementedError(f"Adding extendable generators for carrier "
"'{tech}' is not implemented, yet. "
"Only OCGT, CCGT and nuclear are allowed at the moment.")
def attach_OPSD_renewables(n, tech_map):
tech_string = ", ".join(sum(tech_map.values(), []))
logger.info(f'Using OPSD renewable capacities for carriers {tech_string}.')
df = pm.data.OPSD_VRE().powerplant.convert_country_to_alpha2()
technology_b = ~df.Technology.isin(['Onshore', 'Offshore'])
df['Fueltype'] = df.Fueltype.where(technology_b, df.Technology).replace({"Solar": "PV"})
df = df.query('Fueltype in @tech_map').powerplant.convert_country_to_alpha2()
for fueltype, carriers in tech_map.items():
gens = n.generators[lambda df: df.carrier.isin(carriers)]
buses = n.buses.loc[gens.bus.unique()]
gens_per_bus = gens.groupby('bus').p_nom.count()
caps = map_country_bus(df.query('Fueltype == @fueltype'), buses)
caps = caps.groupby(['bus']).Capacity.sum()
caps = caps / gens_per_bus.reindex(caps.index, fill_value=1)
n.generators.p_nom.update(gens.bus.map(caps).dropna())
n.generators.p_nom_min.update(gens.bus.map(caps).dropna())
def estimate_renewable_capacities(n, config):
year = config["electricity"]["estimate_renewable_capacities"]["year"]
tech_map = config["electricity"]["estimate_renewable_capacities"]["technology_mapping"]
countries = config["countries"]
expansion_limit = config["electricity"]["estimate_renewable_capacities"]["expansion_limit"]
if not len(countries) or not len(tech_map): return
capacities = pm.data.IRENASTAT().powerplant.convert_country_to_alpha2()
capacities = capacities.query("Year == @year and Technology in @tech_map and Country in @countries")
capacities = capacities.groupby(["Technology", "Country"]).Capacity.sum()
logger.info(f"Heuristics applied to distribute renewable capacities [GW]: "
f"\n{capacities.groupby('Technology').sum().div(1e3).round(2)}")
for ppm_technology, techs in tech_map.items():
tech_i = n.generators.query('carrier in @techs').index
stats = capacities.loc[ppm_technology].reindex(countries, fill_value=0.)
country = n.generators.bus[tech_i].map(n.buses.country)
existent = n.generators.p_nom[tech_i].groupby(country).sum()
missing = stats - existent
dist = n.generators_t.p_max_pu.mean() * n.generators.p_nom_max
n.generators.loc[tech_i, 'p_nom'] += (
dist[tech_i]
.groupby(country)
.transform(lambda s: normed(s) * missing[s.name])
.where(lambda s: s>0.1, 0.) # only capacities above 100kW
)
n.generators.loc[tech_i, 'p_nom_min'] = n.generators.loc[tech_i, 'p_nom']
if expansion_limit:
assert np.isscalar(expansion_limit)
logger.info(f"Reducing capacity expansion limit to {expansion_limit*100:.2f}% of installed capacity.")
n.generators.loc[tech_i, 'p_nom_max'] = expansion_limit * n.generators.loc[tech_i, 'p_nom_min']
def add_nice_carrier_names(n, config):
carrier_i = n.carriers.index
nice_names = (pd.Series(config['plotting']['nice_names'])
.reindex(carrier_i).fillna(carrier_i.to_series().str.title()))
n.carriers['nice_name'] = nice_names
colors = pd.Series(config['plotting']['tech_colors']).reindex(carrier_i)
if colors.isna().any():
missing_i = list(colors.index[colors.isna()])
logger.warning(f'tech_colors for carriers {missing_i} not defined in config.')
n.carriers['color'] = colors
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('add_electricity')
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.base_network)
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
ppl = load_powerplants(snakemake.input.powerplants)
if "renewable_carriers" in snakemake.config['electricity']:
renewable_carriers = set(snakemake.config['renewable'])
else:
logger.warning("Missing key `renewable_carriers` under config entry `electricity`. "
"In future versions, this will raise an error. "
"Falling back to carriers listed under `renewable`.")
renewable_carriers = snakemake.config['renewable']
extendable_carriers = snakemake.config['electricity']['extendable_carriers']
if not (set(renewable_carriers) & set(extendable_carriers['Generator'])):
logger.warning("No renewables found in config entry `extendable_carriers`. "
"In future versions, these have to be explicitely listed. "
"Falling back to all renewables.")
conventional_carriers = snakemake.config["electricity"]["conventional_carriers"]
attach_load(n, snakemake.input.regions, snakemake.input.load, snakemake.input.nuts3_shapes,
snakemake.config['countries'], snakemake.config['load']['scaling_factor'])
update_transmission_costs(n, costs, snakemake.config['lines']['length_factor'])
conventional_inputs = {k: v for k, v in snakemake.input.items() if k.startswith("conventional_")}
attach_conventional_generators(n, costs, ppl, conventional_carriers, extendable_carriers, snakemake.config.get("conventional", {}), conventional_inputs)
attach_wind_and_solar(n, costs, snakemake.input, renewable_carriers, extendable_carriers, snakemake.config['lines']['length_factor'])
if 'hydro' in renewable_carriers:
conf = snakemake.config['renewable']['hydro']
attach_hydro(n, costs, ppl, snakemake.input.profile_hydro, snakemake.input.hydro_capacities,
conf.pop('carriers', []), **conf)
if "estimate_renewable_capacities" not in snakemake.config['electricity']:
logger.warning("Missing key `estimate_renewable_capacities` under config entry `electricity`. "
"In future versions, this will raise an error. "
"Falling back to whether ``estimate_renewable_capacities_from_capacity_stats`` is in the config.")
if "estimate_renewable_capacities_from_capacity_stats" in snakemake.config['electricity']:
estimate_renewable_caps = {'enable': True, **snakemake.config['electricity']["estimate_renewable_capacities_from_capacity_stats"]}
else:
estimate_renewable_caps = {'enable': False}
else:
estimate_renewable_caps = snakemake.config['electricity']["estimate_renewable_capacities"]
if "enable" not in estimate_renewable_caps:
logger.warning("Missing key `enable` under config entry `estimate_renewable_capacities`. "
"In future versions, this will raise an error. Falling back to False.")
estimate_renewable_caps = {'enable': False}
if "from_opsd" not in estimate_renewable_caps:
logger.warning("Missing key `from_opsd` under config entry `estimate_renewable_capacities`. "
"In future versions, this will raise an error. "
"Falling back to whether `renewable_capacities_from_opsd` is non-empty.")
from_opsd = bool(snakemake.config["electricity"].get("renewable_capacities_from_opsd", False))
estimate_renewable_caps['from_opsd'] = from_opsd
if estimate_renewable_caps["enable"]:
if estimate_renewable_caps["from_opsd"]:
tech_map = snakemake.config["electricity"]["estimate_renewable_capacities"]["technology_mapping"]
attach_OPSD_renewables(n, tech_map)
estimate_renewable_capacities(n, snakemake.config)
update_p_nom_max(n)
add_nice_carrier_names(n, snakemake.config)
n.meta = snakemake.config
n.export_to_netcdf(snakemake.output[0])